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Record W3216977240 · doi:10.1038/s41598-021-02253-8

Modelling ocean acidification effects with life stage-specific responses alters spatiotemporal patterns of catch and revenues of American lobster, Homarus americanus

2021· article· en· W3216977240 on OpenAlexafffund
Travis C. Tai, Piero Calosi, Helen Gurney‐Smith, William W. L. Cheung

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British ColumbiaUniversité du Québec à RimouskiFisheries and Oceans Canada
FundersCompute CanadaSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaOuranosMitacsMarine Environmental Observation Prediction and Response Network
KeywordsHomarusAmerican lobsterOcean acidificationFisheryOceanographyStage (stratigraphy)BiologyEnvironmental scienceEcologyCrustaceanGeologySeawater

Abstract

fetched live from OpenAlex

Ocean acidification (OA) affects marine organisms through various physiological and biological processes, yet our understanding of how these translate to large-scale population effects remains limited. Here, we integrated laboratory-based experimental results on the life history and physiological responses to OA of the American lobster, Homarus americanus, into a dynamic bioclimatic envelope model to project future climate change effects on species distribution, abundance, and fisheries catch potential. Ocean acidification effects on juvenile stages had the largest stage-specific impacts on the population, while cumulative effects across life stages significantly exerted the greatest impacts, albeit quite minimal. Reducing fishing pressure leads to overall increases in population abundance while setting minimum size limits also results in more higher-priced market-sized lobsters (> 1 lb), and could help mitigate the negative impacts of OA and concurrent stressors (warming, deoxygenation). However, the magnitude of increased effects of climate change overweighs any moderate population gains made by changes in fishing pressure and size limits, reinforcing that reducing greenhouse gas emissions is most pressing and that climate-adaptive fisheries management is necessary as a secondary role to ensure population resiliency. We suggest possible strategies to mitigate impacts by preserving important population demographics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.225
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2021
Admission routes2
Has abstractyes

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